Learning by example. Supervised learning is the modern-day implementation of this age-old adage. By providing an algorithm with lots of data points and corresponding labels, it will be able to match labels to a certain patterns in the data. Examples of this classic machine learning problem are seen everywhere, from facial recognition software at the airport to the spam filter in your email client.
In this course, you will get familiar with all the important steps in building a supervised machine learning model: data preparation, model training and validation, and parameter optimization. You will implement and optimize a number of commonly used supervised machine learning algorithms in Python’s scikit-learn, and learn about the benefits of each model. After the course, you will understand the core concepts and algorithms of supervised learning.